1 Introduction

1.1 Contexte et Objectifs

Le dioxyde de carbone (CO₂) est l’un des gaz à effet de serre les plus importants. Sa concentration atmosphérique a augmenté de manière significative depuis l’ère industrielle. Ce rapport analyse les données globales de concentrations de CO₂ collectées par le NOAA Global Monitoring Laboratory de 1979 à 2025.

1.1.1 Objectifs de l’analyse:

  • Étudier l’évolution temporelle des concentrations de CO₂
  • Analyser les tendances et les taux de croissance
  • Examiner la saisonnalité et ses variations
  • Fournir des statistiques descriptives complètes

2 Méthodologie

2.1 1. Installation et Chargement des Packages

# Liste des packages nécessaires
packages <- c("tidyverse", "lubridate", "ggplot2", "ggpubr", "zoo", "gridExtra",
              "viridis", "scales", "forecast", "grid", "knitr", "kableExtra")

# Installer les packages manquants
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]

if(length(new_packages)) {
  install.packages(new_packages, repos = "https://cloud.r-project.org/")
}

# Charger tous les packages
invisible(lapply(packages, library, character.only = TRUE))

cat("✓ Tous les packages sont chargés\n")
## ✓ Tous les packages sont chargés

2.2 2. Lecture et Inspection des Données

# Vérifier l'existence du fichier
if(!file.exists("co2_mm_gl.csv")) {
  stop("ERREUR: Fichier co2_mm_gl.csv non trouvé!")
}

# Inspection des premières lignes
cat("=== INSPECTION DES DONNÉES BRUTES ===\n\n")
## === INSPECTION DES DONNÉES BRUTES ===
raw_lines <- readLines("co2_mm_gl.csv", n = 10)
cat("Premières lignes du fichier:\n")
## Premières lignes du fichier:
for(i in 1:length(raw_lines)) {
  cat(sprintf("Ligne %2d: %s\n", i, substr(raw_lines[i], 1, 100)))
}
## Ligne  1: # --------------------------------------------------------------------
## Ligne  2: # USE OF NOAA GML DATA
## Ligne  3: #
## Ligne  4: # These data are made freely available to the public and the scientific
## Ligne  5: # community in the belief that their wide dissemination will lead to
## Ligne  6: # greater understanding and new scientific insights. To ensure that GML
## Ligne  7: # receives fair credit for their work please include relevant citation
## Ligne  8: # text in publications. We encourage users to contact the data providers,
## Ligne  9: # who can provide detailed information about the measurements and
## Ligne 10: # scientific insight.  In cases where the data are central to a
# Lire les données (ignorer les lignes de commentaire)
co2_raw <- read.table("co2_mm_gl.csv",
                      header = TRUE,
                      sep = ",",
                      comment.char = "#",
                      stringsAsFactors = FALSE,
                      na.strings = c("", "NA", "-99.99"))

cat("\n=== DONNÉES BRUTES CHARGÉES ===\n")
## 
## === DONNÉES BRUTES CHARGÉES ===
cat("Dimensions:", dim(co2_raw)[1], "lignes x", dim(co2_raw)[2], "colonnes\n")
## Dimensions: 561 lignes x 7 colonnes
cat("Période:", min(co2_raw$year), "à", max(co2_raw$year), "\n")
## Période: 1979 à 2025
cat("Colonnes:", paste(names(co2_raw), collapse = ", "), "\n")
## Colonnes: year, month, decimal, average, average_unc, trend, trend_unc

2.3 3. Nettoyage et Préparation des Données

co2_clean <- co2_raw %>%
  # Créer une date complète
  mutate(
    Date = make_date(year, month, 15),  # 15 du mois comme convention
    Date_decimal = decimal,
    
    # Variables principales
    CO2_ppm = average,  # Concentration CO₂
    CO2_uncertainty = average_unc,  # Incertitude
    CO2_trend = trend,  # Tendance désaisonnalisée
    Trend_uncertainty = trend_unc,
    
    # Identifiants et métadonnées
    ID = row_number(),
    
    # Variables temporelles
    Year = year,
    Month = month,
    Quarter = ceiling(month / 3),
    
    # Saisons (hémisphère nord)
    Season = case_when(
      month %in% c(12, 1, 2) ~ "Hiver",
      month %in% c(3, 4, 5) ~ "Printemps",
      month %in% c(6, 7, 8) ~ "Été",
      month %in% c(9, 10, 11) ~ "Automne"
    ),
    
    # Décennies
    Decade = floor(Year / 10) * 10,
    
    # Périodes d'analyse
    Period = case_when(
      Year < 1990 ~ "1979-1989",
      Year >= 1990 & Year < 2000 ~ "1990-1999",
      Year >= 2000 & Year < 2010 ~ "2000-2009",
      Year >= 2010 & Year < 2020 ~ "2010-2019",
      Year >= 2020 ~ "2020-2025"
    ),
    
    # Variables dérivées
    CO2_anomaly = CO2_ppm - mean(CO2_ppm, na.rm = TRUE),
    Year_fraction = Year + (Month - 0.5) / 12
  ) %>%
  
  # Gérer les valeurs manquantes
  group_by(Year) %>%
  mutate(
    CO2_ppm = ifelse(is.na(CO2_ppm), mean(CO2_ppm, na.rm = TRUE), CO2_ppm)
  ) %>%
  ungroup() %>%
  
  # Sélectionner et ordonner les colonnes
  select(ID, Date, Year, Month, Quarter, Season, Decade, Period,
         CO2_ppm, CO2_uncertainty, CO2_trend, Trend_uncertainty,
         CO2_anomaly, Date_decimal, Year_fraction) %>%
  
  # Trier par date
  arrange(Date)

cat("✓ Données nettoyées\n")
## ✓ Données nettoyées
cat("Observations:", nrow(co2_clean), "\n")
## Observations: 561
cat("Période finale:", min(co2_clean$Year), "-", max(co2_clean$Year), "\n")
## Période finale: 1979 - 2025
cat("Valeurs manquantes CO2:", sum(is.na(co2_clean$CO2_ppm)), "\n")
## Valeurs manquantes CO2: 0

3 Résultats

3.1 1. Statistiques Descriptives

3.1.1 1.1 Statistiques Générales

general_stats <- summary(co2_clean$CO2_ppm)

stats_table <- data.frame(
  "Statistique" = c("Minimum", "Quartile 1", "Médiane", "Moyenne", "Quartile 3", "Maximum"),
  "Valeur (ppm)" = c(
    round(general_stats[1], 2),
    round(general_stats[2], 2),
    round(general_stats[3], 2),
    round(general_stats[4], 2),
    round(general_stats[5], 2),
    round(general_stats[6], 2)
  )
)

knitr::kable(stats_table, caption = "Statistiques descriptives du CO₂ global") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
Statistiques descriptives du CO₂ global
Statistique Valeur..ppm.
Min. Minimum 334.37
1st Qu. Quartile 1 354.33
Median Médiane 372.51
Mean Moyenne 375.67
3rd Qu. Quartile 3 396.38
Max. Maximum 426.87

3.1.2 1.2 Statistiques par Décennie

decade_stats <- co2_clean %>%
  group_by(Decade) %>%
  summarise(
    "Décennie" = paste0(Decade, "s"),
    "Début" = min(Year),
    "Fin" = max(Year),
    "N mois" = n(),
    "CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
    "Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
    "Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
    "Écart-type" = round(sd(CO2_ppm, na.rm = TRUE), 2),
    "Augmentation (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
    .groups = 'drop'
  ) %>%
  select(-Decade)

knitr::kable(decade_stats, caption = "Statistiques du CO₂ par décennie") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Statistiques du CO₂ par décennie
Décennie Début Fin N mois CO₂ moyen (ppm) Min (ppm) Max (ppm) Écart-type Augmentation (ppm)
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1970s 1979 1979 12 336.86 334.37 338.32 1.30 3.95
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1980s 1980 1989 120 345.16 337.05 354.38 4.66 17.33
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
1990s 1990 1999 120 359.93 351.58 369.29 4.58 17.71
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2000s 2000 2009 120 377.87 366.71 387.99 5.94 21.28
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2010s 2010 2019 120 399.04 386.23 411.76 7.11 25.53
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14
2020s 2020 2025 69 418.34 409.73 426.87 4.60 17.14

3.1.3 1.3 Statistiques Saisonnières

season_stats <- co2_clean %>%
  group_by(Season) %>%
  summarise(
    "CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
    "Écart-type" = round(sd(CO2_ppm, na.rm = TRUE), 2),
    "Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
    "Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
    "Amplitude (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
    .groups = 'drop'
  ) %>%
  arrange(`CO₂ moyen (ppm)`)

knitr::kable(season_stats, caption = "Statistiques saisonnières du CO₂") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Statistiques saisonnières du CO₂
Season CO₂ moyen (ppm) Écart-type Min (ppm) Max (ppm) Amplitude (ppm)
Automne 374.32 25.42 335.02 424.07 89.05
Été 374.96 25.76 334.37 425.87 91.50
Hiver 376.21 25.70 336.56 426.42 89.86
Printemps 377.16 25.83 337.88 426.87 88.99

3.1.4 1.4 Top 5 des Années avec Concentrations les Plus Élevées

annual_stats <- co2_clean %>%
  group_by(Year) %>%
  summarise(
    "CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
    "Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
    "Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
    "Amplitude (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
    .groups = 'drop'
  ) %>%
  arrange(desc(`CO₂ moyen (ppm)`))

knitr::kable(head(annual_stats, 5), caption = "Top 5 des années avec concentrations les plus élevées") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Top 5 des années avec concentrations les plus élevées
Year CO₂ moyen (ppm) Min (ppm) Max (ppm) Amplitude (ppm)
2025 425.43 423.03 426.87 3.84
2024 422.80 420.27 425.19 4.92
2023 419.37 416.79 421.50 4.71
2022 417.09 414.40 418.93 4.53
2021 414.70 412.15 416.60 4.45

3.2 2. Analyses Avancées

3.2.1 2.1 Analyse de Tendance Linéaire

# Régression linéaire
lm_model <- lm(CO2_ppm ~ Year_fraction, data = co2_clean)
lm_summary <- summary(lm_model)

trend_results <- data.frame(
  "Paramètre" = c("Pente", "Ordonnée à l'origine", "R²", "p-value"),
  "Valeur" = c(
    round(coef(lm_model)[2], 4),
    round(coef(lm_model)[1], 2),
    round(lm_summary$r.squared, 4),
    format.pval(lm_summary$coefficients[2, 4], digits = 3)
  ),
  "Interprétation" = c(
    "ppm/année",
    "ppm (1900)",
    "ajustement du modèle",
    "très significatif"
  )
)

knitr::kable(trend_results, caption = "Résultats de la régression linéaire CO₂ ~ Temps") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Résultats de la régression linéaire CO₂ ~ Temps
Paramètre Valeur Interprétation
Pente 1.8838 ppm/année
Ordonnée à l’origine -3396.42 ppm (1900)
R² 0.9853 ajustement du modèle
p-value <0.0000000000000002 très significatif

Interprétation: La concentration de CO₂ augmente de 1.884 ppm par année avec une très bonne corrélation (R² = 0.985).

3.2.2 2.2 Augmentation Totale (1979-2025)

co2_1979 <- mean(co2_clean$CO2_ppm[co2_clean$Year == 1979], na.rm = TRUE)
co2_2025 <- mean(co2_clean$CO2_ppm[co2_clean$Year == 2025], na.rm = TRUE)
augmentation_abs <- co2_2025 - co2_1979
augmentation_pct <- (co2_2025 - co2_1979) / co2_1979 * 100
taux_annuel <- (co2_2025 - co2_1979) / (2025 - 1979)

augmentation_table <- data.frame(
  "Année" = c("1979", "2025", "Différence"),
  "CO₂ (ppm)" = c(
    round(co2_1979, 1),
    round(co2_2025, 1),
    round(augmentation_abs, 1)
  ),
  "% d'augmentation" = c(
    "-",
    "-",
    round(augmentation_pct, 1)
  )
)

knitr::kable(augmentation_table, caption = "Augmentation du CO₂ de 1979 à 2025") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Augmentation du CO₂ de 1979 à 2025
Année CO…ppm. X..d.augmentation
1979 336.9
2025 425.4
Différence 88.6 26.3 
cat("\n**Taux annuel moyen:** ", round(taux_annuel, 2), " ppm/an\n")
## 
## **Taux annuel moyen:**  1.93  ppm/an

3.2.3 2.3 Analyse de Saisonnalité

seasonal_amplitude <- max(season_stats$`CO₂ moyen (ppm)`) - min(season_stats$`CO₂ moyen (ppm)`)

saisonnalite_table <- data.frame(
  "Saison la plus élevée" = season_stats$Season[which.max(season_stats$`CO₂ moyen (ppm)`)],
  "CO₂ max (ppm)" = round(max(season_stats$`CO₂ moyen (ppm)`), 2),
  "Saison la plus basse" = season_stats$Season[which.min(season_stats$`CO₂ moyen (ppm)`)],
  "CO₂ min (ppm)" = round(min(season_stats$`CO₂ moyen (ppm)`), 2),
  "Amplitude (ppm)" = round(seasonal_amplitude, 2)
)

knitr::kable(saisonnalite_table, caption = "Analyse de la saisonnalité du CO₂") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Analyse de la saisonnalité du CO₂
Saison.la.plus.élevée CO..max..ppm. Saison.la.plus.basse CO..min..ppm. Amplitude..ppm.
Printemps 377.16 Automne 374.32 2.84

3.3 3. Visualisations

3.3.1 3.1 Évolution Temporelle Complète (1979-2025)

p1 <- ggplot(co2_clean, aes(x = Date, y = CO2_ppm)) +
  geom_line(color = "#E41A1C", linewidth = 1, alpha = 0.8) +
  geom_smooth(method = "loess", span = 0.1, color = "#377EB8",
              linetype = "dashed", se = FALSE, linewidth = 1) +
  geom_smooth(method = "lm", color = "#4DAF4A", se = FALSE, linewidth = 0.8) +
  labs(
    title = "Évolution des concentrations CO₂ globales (1979-2025)",
    subtitle = "Source: NOAA Global Monitoring Laboratory",
    x = "Année",
    y = "CO₂ (ppm)",
    caption = paste("Pente:", round(coef(lm_model)[2], 3), "ppm/an | R²:", round(lm_summary$r.squared, 3))
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5, size = 16),
    plot.subtitle = element_text(hjust = 0.5, color = "gray40"),
    plot.caption = element_text(hjust = 1, color = "gray50"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "white", color = NA)
  ) +
  scale_y_continuous(limits = c(330, 440)) +
  scale_x_date(date_breaks = "5 years", date_labels = "%Y")

print(p1)

Commentaire: Ce graphique démontre l’augmentation anthropogénique incontestable du CO₂ à l’échelle globale, avec une tendance robuste et systématique sur 46 ans.

3.3.2 3.2 Cycle Saisonnier du CO₂

p2 <- ggplot(co2_clean, aes(x = Month, y = CO2_ppm)) +
  geom_jitter(alpha = 0.1, color = "gray70", width = 0.2) +
  stat_summary(fun = mean, geom = "line", aes(group = 1),
               color = "#984EA3", linewidth = 1.5) +
  stat_summary(fun = mean, geom = "point", aes(group = 1),
               color = "#984EA3", size = 3) +
  stat_summary(fun.data = mean_cl_normal, geom = "ribbon",
               aes(group = 1), alpha = 0.2, fill = "#984EA3") +
  labs(
    title = "Cycle saisonnier du CO₂ global",
    subtitle = "Moyenne mensuelle avec intervalle de confiance",
    x = "Mois",
    y = "CO₂ (ppm)"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  scale_x_continuous(breaks = 1:12, labels = month.abb)

print(p2)

Commentaire:Ce graphique révèle le cycle saisonnier du CO₂ en montrant la moyenne mensuelle sur l’ensemble de la période 1979-2025. Le cycle saisonnier montre un maximum au printemps et un minimum en automne, reflétant les cycles de photosynthèse de l’hémisphère nord. Ce graphique démontre que malgré l’augmentation globale du CO₂, les cycles biologiques naturels continuent de fonctionner régulièrement, créant une oscillation saisonnière mesurable et fiable.

3.3.3 3.3 Évolution du Cycle Saisonnier par Décennie

seasonal_by_decade <- co2_clean %>%
  group_by(Decade, Month) %>%
  summarise(
    CO2_mean = mean(CO2_ppm, na.rm = TRUE),
    CO2_sd = sd(CO2_ppm, na.rm = TRUE),
    N = n(),
    .groups = 'drop'
  ) %>%
  mutate(
    Month_name = factor(month.abb[Month], levels = month.abb),
    Decade_label = paste0(Decade, "s")
  )

p3 <- ggplot(seasonal_by_decade, aes(x = Month_name, y = CO2_mean,
                                      color = Decade_label, group = Decade_label)) +
  geom_line(linewidth = 1.2, alpha = 0.8) +
  geom_point(size = 2.5) +
  labs(
    title = "Évolution du cycle saisonnier par décennie",
    subtitle = "Moyenne mensuelle pour chaque décennie (1979-2025)",
    x = "Mois",
    y = "CO₂ (ppm)",
    color = "Décennie"
  ) +
  scale_color_viridis_d(option = "D", begin = 0.2, end = 0.9) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5, size = 15),
    plot.subtitle = element_text(hjust = 0.5, color = "gray40"),
    legend.position = "right",
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  scale_x_discrete(limits = month.abb)

print(p3)

Commentaire:Ce graphique superpose les cycles saisonniers de six décennies pour montrer comment le phénomène naturel du cycle du CO₂ a évolué au fil du temps. Chaque décennie “ajoute une marche” à l’escalier du CO₂ atmosphérique, confirmant une augmentation systématique et inexorable depuis 1979.

3.3.4 3.4 Distribution du CO₂ par Décennie

p4 <- ggplot(co2_clean, aes(x = factor(Decade), y = CO2_ppm, fill = factor(Decade))) +
  geom_boxplot(alpha = 0.7, outlier.alpha = 0.3) +
  stat_summary(fun = mean, geom = "point", shape = 23, size = 3, fill = "white") +
  labs(
    title = "Distribution du CO₂ par décennie",
    subtitle = "Boîtes: distribution interquartile | Losanges: moyenne",
    x = "Décennie",
    y = "CO₂ (ppm)",
    fill = "Décennie"
  ) +
  scale_fill_viridis_d(option = "C", begin = 0.2, end = 0.9) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  scale_y_continuous(limits = c(330, 440))

print(p4)

Commentaire:Ce graphique est une transformation mathématique du précédent qui révèle une vérité importante : il montre l’anomalie du CO₂, c’est-à-dire l’écart par rapport à la normale climatologique mensuelle. En d’autres termes, il supprime les variations saisonnières pour ne montrer que le “surplus de CO₂” au-delà de ce qui est attendu pour chaque mois.

3.3.5 3.5 Anomalies du CO₂ par Rapport à la Moyenne Mensuelle

co2_monthly_avg <- co2_clean %>%
  group_by(Month) %>%
  summarise(monthly_avg = mean(CO2_ppm, na.rm = TRUE))

co2_anomalies <- co2_clean %>%
  left_join(co2_monthly_avg, by = "Month") %>%
  mutate(anomaly = CO2_ppm - monthly_avg)

p5 <- ggplot(co2_anomalies, aes(x = Date, y = anomaly)) +
  geom_line(color = "#FF7F00", linewidth = 0.7, alpha = 0.7) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
  geom_smooth(method = "loess", span = 0.2, color = "#A65628", se = FALSE) +
  labs(
    title = "Anomalies du CO₂ par rapport à la moyenne mensuelle",
    subtitle = "Écart à la moyenne climatologique mensuelle",
    x = "Année",
    y = "Anomalie CO₂ (ppm)"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
    plot.subtitle = element_text(hjust = 0.5, color = "gray40")
  ) +
  scale_x_date(date_breaks = "5 years", date_labels = "%Y")

print(p5)

Commentaire:Ce graphique utilise un diagramme en boîte (boxplot) pour comparer la distribution du CO₂ à travers six décennies.Chaque génération depuis 1979 a vécu dans une atmosphère avec ~20 ppm de CO₂ supplémentaire par rapport à la génération précédente.

3.3.6 3.6 Taux de Croissance Annuel du CO₂

annual_growth <- co2_clean %>%
  group_by(Year) %>%
  summarise(mean_co2 = mean(CO2_ppm, na.rm = TRUE)) %>%
  mutate(
    growth = mean_co2 - lag(mean_co2),
    growth_rate = (mean_co2 - lag(mean_co2)) / lag(mean_co2) * 100
  ) %>%
  filter(!is.na(growth))

p6 <- ggplot(annual_growth, aes(x = Year, y = growth)) +
  geom_col(fill = "#F781BF", alpha = 0.7) +
  geom_hline(yintercept = mean(annual_growth$growth, na.rm = TRUE),
             linetype = "dashed", color = "#E41A1C", linewidth = 1) +
  geom_smooth(method = "loess", color = "#377EB8", se = FALSE, linewidth = 0.8) +
  labs(
    title = "Augmentation annuelle du CO₂",
    subtitle = paste("Moyenne:", round(mean(annual_growth$growth, na.rm = TRUE), 2), "ppm/an"),
    x = "Année",
    y = "Augmentation (ppm/an)"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5, color = "gray40")
  ) +
  scale_x_continuous(breaks = seq(1980, 2025, by = 5))

print(p6)

Commentaire:Ce graphique montre l’augmentation année-par-année du CO₂. Bien que l’augmentation du CO₂ varie d’année en année, la tendance générale (ligne bleue) montre clairement une accélération du taux d’augmentation. Le problème ne s’améliore pas ; il s’aggrave. La moyenne de 1.93 ppm/an masque le fait que les années récentes augmentent plus vite (~2.7 ppm/an), ce qui indique une urgence croissante de la crise climatique.

3.4 4. Tableaux Synthétiques Avancés

3.4.1 4.1 Amplitude Saisonnière par Décennie

seasonal_amplitude_by_decade <- seasonal_by_decade %>%
  group_by(Decade, Decade_label) %>%
  summarise(
    "Amplitude (ppm)" = round(max(CO2_mean) - min(CO2_mean), 2),
    "CO₂ min (ppm)" = round(min(CO2_mean), 2),
    "CO₂ max (ppm)" = round(max(CO2_mean), 2),
    .groups = 'drop'
  ) %>%
  select(-Decade)

knitr::kable(seasonal_amplitude_by_decade, 
             caption = "Amplitude saisonnière du CO₂ par décennie") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Amplitude saisonnière du CO₂ par décennie
Decade_label Amplitude (ppm) CO₂ min (ppm) CO₂ max (ppm)
1970s 3.95 334.37 338.32
1980s 3.49 343.05 346.54
1990s 3.77 357.61 361.38
2000s 3.79 375.47 379.26
2010s 4.11 396.54 400.65
2020s 3.88 416.06 419.94

3.4.2 4.2 Taux de Croissance du CO₂ par Décennie

decade_slopes <- co2_clean %>%
  group_by(Decade) %>%
  do(
    data.frame(
      Decade = unique(.$Decade),
      Pente_ppm_par_an = round(coef(lm(CO2_ppm ~ Year_fraction, data = .))[2], 4),
      R_squared = round(summary(lm(CO2_ppm ~ Year_fraction, data = .))$r.squared, 4)
    )
  )

knitr::kable(decade_slopes, 
             caption = "Pente de croissance du CO₂ par décennie",
             col.names = c("Décennie", "Pente (ppm/an)", "R²")) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
Pente de croissance du CO₂ par décennie
Décennie Pente (ppm/an) R²
1970 -1.0645 0.0608
1980 1.5231 0.8969
1990 1.4697 0.8652
2000 1.9699 0.9255
2010 2.3845 0.9444
2020 2.5396 0.8529

4 Discussion

4.1 Principaux Résultats

  1. Tendance générale: Les concentrations de CO₂ augmentent de manière consistante et significative, avec une pente de 1.884 ppm par année.

  2. Augmentation totale: Depuis 1979, le CO₂ a augmenté de 88.6 ppm, soit une augmentation relative de 26.3%.

  3. Saisonnalité: L’amplitude saisonnière moyenne est de 2.84 ppm, le maximum étant atteint au Printemps et le minimum en Automne.

  4. Accélération: L’analyse des taux de croissance annuels montre une variabilité d’une année à l’autre, avec une tendance à l’augmentation globale.

4.2 Implications

  • Les données confirment l’augmentation anthropogénique du CO₂ observée mondialement
  • La régularité de cette augmentation souligne la nature systématique du problème
  • La saisonnalité reflète les cycles biologiques naturels (photosynthèse, respiration)

4.3 Limitations et Perspectives

  • Les données proviennent d’une seule source (NOAA), mais celle-ci est reconnue internationalement
  • Les analyses futures pourraient intégrer d’autres variables climatiques (température, humidité)
  • Une comparaison régionale avec d’autres stations de mesure enrichirait l’analyse

5 Conclusion

Cette analyse des données NOAA (1979-2025) démontre sans ambiguïté l’augmentation continue et significative des concentrations de CO₂ atmosphérique à l’échelle mondiale. Avec une pente linéaire de 1.88 ppm/an et un excellent ajustement statistique (R² = 0.985), les résultats confirment que cette hausse n’est pas due au hasard ou à la variabilité naturelle, mais représente une tendance structurelle majeure. L’augmentation totale de 88.6 ppm en 46 ans (26% d’accroissement relatif) constitue une signature incontestable du changement climatique anthropogénique. Bien que la saisonnalité naturelle soit préservée (cycle régulier de ~2.8 ppm), elle survient désormais sur un niveau de base en hausse permanente, escalade décennie par décennie. Ces observations confirment l’urgence d’une action globale pour réduire les émissions de CO₂, car le système climatique répond directement et proportionnellement à l’accumulation de ce gaz à effet de serre.

6 Annexe: Code Complet du Nettoyage

# Script complet de préparation des données

# 1. Packages
packages <- c("tidyverse", "lubridate", "ggplot2", "ggpubr", "zoo", "gridExtra",
              "viridis", "scales", "forecast", "grid", "knitr", "kableExtra")
invisible(lapply(packages, library, character.only = TRUE))

# 2. Lecture
co2_raw <- read.table("co2_mm_gl.csv",
                      header = TRUE,
                      sep = ",",
                      comment.char = "#",
                      stringsAsFactors = FALSE,
                      na.strings = c("", "NA", "-99.99"))

# 3. Nettoyage
co2_clean <- co2_raw %>%
  mutate(
    Date = make_date(year, month, 15),
    CO2_ppm = average,
    CO2_trend = trend,
    CO2_anomaly = average - mean(average, na.rm = TRUE),
    Year = year,
    Month = month,
    Decade = floor(year / 10) * 10,
    Year_fraction = year + (month - 0.5) / 12,
    Season = case_when(
      month %in% c(12, 1, 2) ~ "Hiver",
      month %in% c(3, 4, 5) ~ "Printemps",
      month %in% c(6, 7, 8) ~ "Été",
      month %in% c(9, 10, 11) ~ "Automne"
    )
  ) %>%
  group_by(Year) %>%
    mutate(
      CO2_ppm = ifelse(is.na(CO2_ppm), mean(CO2_ppm, na.rm = TRUE), CO2_ppm)
    ) %>%
  ungroup() %>%
  select(Date, Year, Month, CO2_ppm, CO2_trend, CO2_anomaly, Season, Decade, Year_fraction) %>%
  arrange(Date)

Rapport généré le: 16 December 2025
Source des données: NOAA Global Monitoring Laboratory
Période analysée: 1979-2025